Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| CatBoost× | Random Forest× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2018 | 2001 |
| Autor original≠ | Prokhorenkova, L. et al. (Yandex) | Breiman, L. |
| Tipo≠ | Gradient boosting on decision trees | Ensemble (bagging of decision trees) |
| Fuente seminal≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateConjunto de datos ↗ |
|
|